Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer’s disease
Open accessAbstract
Although previous studies have emphasized the vulnerability of the default mode network (DMN) in Alzheimer’s disease (AD), little is known about the involvement of other functional networks and their relationship to clinical phenotype. To test whether clinicoanatomic heterogeneity in AD is driven by the involvement of specific networks, network connectivity was assessed in healthy subjects by seeding regions commonly and specifically atrophied in three clinical AD variants: early-onset AD (age at onset, <65 y; memory and executive deficits), logopenic variant primary progressive aphasia (language deficits), and posterior cortical atrophy (visuospatial deficits). Four-millimeter seed regions of interest were used to obtain intrinsic connectivity maps in 131 healthy controls (age, 65.5 ± 3.5 y). Atrophy patterns in independent cohorts of AD variant patients and their correspondence to connectivity networks in controls were also assessed. The connectivity maps of commonly atrophied regions of interest support posterior DMN and precuneus network involvement across AD variants, whereas seeding regions specifically atrophied in each AD variant revealed distinct, syndrome-specific connectivity patterns. Goodness-of-fit analysis of each connectivity map with network templates showed the highest correspondence between the early-onset AD seed connectivity map and anterior salience and right executive-control networks, the logopenic aphasia seed connectivity map and the language network, and the posterior cortical atrophy seed connectivity map and the higher visual network. Connectivity maps derived from controls matched regions commonly and specifically atrophied in the patients. Our findings indicate that the posterior DMN and precuneus network are commonly affected in AD variants, whereas syndrome-specific neurodegenerative patterns are driven by the involvement of specific networks outside the DMN.
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Previous research has demonstrated lower connectivity among regions involved in specific brain networks of patients with AD, including the default mode network.
“Previous research has demonstrated lower connectivity among regions involved in specific brain networks of patients with AD, including the default mode network (Bai et al., 2011; Damoiseaux, Prater, Miller, & Greicius, 2012; Lehmann et al., 2013; Simic, Babic, Borovecki, & Hof, 2014).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Lehmann et al., 2013
An alternative explanation is that Aβ accumulation is caused by high metabolic demand and metabolic stress in these neurons and not neuronal activation per se.
“An alternative explanation is that Aβ accumulation is caused by the high metabolic demand and metabolic stress in these neurons (high energy consumption and frequent fluctuations in activation and deactivation) and not the neuronal activation per se30.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [30]
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Self-citationMemory loss in Alzheimer's disease
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Imaging the evolution and pathophysiology of Alzheimer disease
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Self-citationA clinicopathological approach to the diagnosis of dementia
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Brain connectivity in neurodegenerative diseases—from phenotype to proteinopathy
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Interpreting and Utilising Intersubject Variability in Brain Function
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The human connectome in Alzheimer disease — relationship to biomarkers and genetics
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Self-citationNetwork Dysfunction in Alzheimer's Disease: Refining the Disconnection Hypothesis
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Network localization of heterogeneous neuroimaging findings
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Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease
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Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer’s disease
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Self-citationMapping Neurodegenerative Disease Onset and ProgressionPreprint
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Preferential degradation of cognitive networks differentiates Alzheimer’s disease from ageing
Chhatwal, J. P., Schultz, A. P., Johnson, K. A., Hedden, T., Jaimes, S., Benzinger, T. L. S., Jack, C., Ances, B. M., Ringman, J. M., Marcus, D. S., et al. (2018) · Brain · doi:10.1093/brain/awy053 · Cited by 101 · see citing works
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Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review
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Mapping the Progression of Atrophy in Early- and Late-Onset Alzheimer’s Disease
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Self-citationMolecular imaging in dementia: Past, present, and future
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Self-citationPrimary progressive aphasia: a model for neurodegenerative disease
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Understanding the cause of sporadic Alzheimer’s disease
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Early Failure of the Default‐Mode Network and the Pathogenesis of Alzheimer's Disease
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Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology
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Selective vulnerability in neurodegeneration: insights from clinical variants of Alzheimer's disease
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White Matter Degeneration in Atypical Alzheimer Disease
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Auditory spatial processing in Alzheimer’s disease
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Tau and atrophy: domain-specific relationships with cognition
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Amyloid Imaging in Atypical Presentations of Alzheimer’s Disease
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Neuroimaging in aging and neurologic diseases
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Breakdown of specific functional brain networks in clinical variants of Alzheimer’s disease
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Disruptions of the olfactory and default mode networks in Alzheimer's disease
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Regional associations of white matter hyperintensities and early cortical amyloid pathology
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Anterior dorsal attention network tau drives visual attention deficits in posterior cortical atrophy
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Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease
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Tract-defined regional white matter hyperintensities and memory
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“Previous research has demonstrated lower connectivity among regions involved in specific brain networks of patients with AD, including the default mode network (Bai et al., 2011; Damoiseaux, Prater, Miller, & Greicius, 2012; Lehmann et al., 2013; Simic, Babic, Borovecki, & Hof, 2014).”
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Klupp, E., Förster, S., Grimmer, T., Tahmasian, M., Yakushev, I. Y., Sorg, C., Yousefi, B. H., & Drzezga, A. E. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0212 · Cited by 31 · see citing works
Effects of bilingualism on age at onset in two clinical Alzheimer's disease variants
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Network anatomy in logopenic variant of primary progressive aphasia
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Self-citationVulnerability of multiple large‐scale brain networks in dementia with Lewy bodies
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3D Shape Perception in Posterior Cortical Atrophy: A Visual Neuroscience Perspective
Gillebert, C. R., Schaeverbeke, J. M., Bastin, C., Neyens, V., Bruffaerts, R., Weer, A. D., Seghers, A., Sunaert, S. G., Laere, K. V., Versijpt, J. J., et al. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3651-14.2015 · Cited by 29 · see citing works
Intrinsic connectivity networks in posterior cortical atrophy: A role for the pulvinar?
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Self-citationIntrinsic connectivity of neural networks in the awake rabbit
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Network Localization of Alien Limb in Patients with Corticobasal Syndrome
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Altered within- and between-network functional connectivity in atypical Alzheimer’s disease
Singh, N. A., Martin, P. R., Graff‐Radford, J., Sintini, I., Machulda, M. M., Duffy, J. R., Gunter, J. L., Botha, H., Jones, D. T., Lowe, V. J., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad184 · Cited by 24 · see citing works
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Future Directions in Imaging Neurodegeneration
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Functional brain network centrality is related to APOE genotype in cognitively normal elderly
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Prodromal posterior cortical atrophy: clinical, neuropsychological, and radiological correlation
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Self-citationDifferences in topological progression profile among neurodegenerative diseases from imaging data
Garbarino, S., Lorenzi, M., Oxtoby, N. P., Vinke, E. J., Marinescu, R. V., Eshaghi, A., Ikram, M. A., Niessen, W. J., Ciccarelli, O., Barkhof, F., et al. (2019) · eLife · doi:10.7554/elife.49298 · Cited by 15 · see citing works
Anticholinergic Amnesia is Mediated by Alterations in Human Network Connectivity Architecture
Chhatwal, J. P., Schultz, A. P., Hedden, T., Boot, B. P., Wigman, S. E., Rentz, D. M., Johnson, K. A., & Sperling, R. A. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy214 · Cited by 15 · see citing works
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Self-citationMethylene blue modulates functional connectivity in the human brain
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Self-citationClinical implications of head trauma in frontotemporal dementia and primary progressive aphasia
Asken, B. M., Bove, J., Bauer, R. M., Tanner, J. A., Casaletto, K. B., Staffaroni, A. M., VandeVrede, L. A., Alosco, M. L., Mez, J., Stern, R. A., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01553-1 · Cited by 12 · see citing works
Self-citationThe Brain Network Hub Degeneration in Alzheimer’s Disease
Jin, S., Wang, J., & Yong He, A. (2024) · Biophysics Reports · doi:10.52601/bpr.2024.230025 · Cited by 11 · see citing works
Shepherd, T. M., & Nayak, G. K. (2019) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000225 · Cited by 11 · see citing works
Brain State Transition Disruptions in Alzheimer's Disease: Insights from EEG State Dynamics
Razavi, S. M., Tran, T. M., Wilson, S. E., & Khanmohammadi, S. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981035 · Cited by 9 · see citing works
The predictive value of normal EEGs in dementia due to Alzheimer’s disease
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- Authors
- Lehmann, M., Madison, C. M., Ghosh, P. M., Seeley, W. W., Mormino, E., Greicius, M. D., Gorno-Tempini, M. L., Kramer, J. H., Miller, B. L., Jagust, W. J., & Rabinovici, G. D.
- Affiliations
- University of California, San Francisco (US)
- University of California, Berkeley (US)
- Stanford Medicine (US)
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94158;
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-06-24
- Volume
- 110
- Issue
- 28
- Pages
- 11606-11611
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3710820
- OpenAlex
- W1999605218
- Cited by
- 122
- References
- 73
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 5 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 17 · 2018: 14 · 2017: 10 · 2016: 15 · 2015: 13 · 2014: 11 · 2013: 4
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Hellman Family Foundation
- National Institute on Aging (P01 AG1972403, K23AG031861, P01AG019724, R01AG034570, P50AG023501)
- National Institute of Neurological Disorders and Stroke (R01NS073498, R01NS050915)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, resting-state networks, default mode network, precuneus, early-onset Alzheimer's disease, logopenic variant primary progressive aphasia, posterior cortical atrophy, brain atrophy, clinical phenotype, salience network, frontoparietal network, language network, healthy controls
- MeSH terms
- Aged, Alzheimer Disease, Brain, Female, Humans, Male, Middle Aged, Nerve Net, Reference Values, Task Performance and Analysis, Cohort Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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